Financial Signals
Financial health signals derived from public company filings, earnings data, analyst consensus, and market data to identify business challenges, growth patterns, and sales opportunities across 75,000+ global companies.
Financial Signals detect meaningful changes in a company's financial trajectory — revenue acceleration, margin compression, growth inflection points, and distress indicators derived from public financial data.
We analyze quarterly and annual financial data from 7,000+ public companies, comparing current performance against historical trends and peer benchmarks. When a company crosses a statistical threshold (e.g., revenue growth accelerating for 3+ consecutive quarters, or margins declining below sector median), we generate a signal. Each company is evaluated weekly and can produce 1-5 signals depending on how many conditions are triggered simultaneously.
See real delivered data → Sample Files
Each financial signal is classified into one of 20+ subtypes based on the specific financial condition detected.
Available Subtypes (20+)
| Subtype Enum | Description |
|---|---|
revenueAcceleration | Revenue growth rate increasing for 2+ consecutive quarters |
revenueDeceleration | Revenue growth rate declining for 2+ consecutive quarters |
marginExpansion | Operating or gross margins expanding quarter-over-quarter |
marginCompression | Operating or gross margins declining quarter-over-quarter |
profitabilityTurnaround | Company crossing from net loss to net profit |
cashBurn | Cash reserves declining at an unsustainable rate |
debtAccumulation | Debt-to-equity ratio increasing significantly |
revenueConcentration | Revenue dependent on few customers or segments |
internationalRevenueShift | International revenue share growing as percentage of total |
recurringRevenueGrowth | Subscription or recurring revenue growing faster than total |
capexSurge | Capital expenditure increasing significantly vs. prior period |
workingCapitalStress | Working capital ratios deteriorating |
earningsBeating | Consistently beating analyst estimates |
earningsMissing | Consistently missing analyst estimates |
growthReacceleration | Growth resuming after a period of stagnation |
distressSignal | Multiple financial indicators pointing to operational stress |
hypergrowth | Revenue growing at 40%+ year-over-year |
dividendChange | Dividend policy change (initiation, increase, or cut) |
buybackAcceleration | Share buyback program accelerating |
segmentOutperformance | One business segment significantly outperforming others |
Example Signal
What a single entry looks like in a delivered signal file:
{
"signal_id": "a92f4d71-8c3e-4b56-9e2a-17d0f3c28b94",
"batch_id": "2026-03-15-00-00-00",
"signal_type": "financial_trends",
"signal_subtype": "revenueAcceleration",
"detected_at": "2026-03-15T06:18:44.782931Z",
"association": "company",
"company": {
"name": "Palantir Technologies Inc.",
"domain": "palantir.com", // match on domain
"linkedin_url": "linkedin.com/company/palantir-technologies", // or match on LinkedIn URL
"industries": ["Software Development"],
"employee_count_low": 3001,
"employee_count_high": 5000,
"description": "AI-powered data analytics and decision..."
},
"contact": [],
"data": {
"summary": "Palantir's commercial revenue is accelerating — 54% YoY growth in Q4 vs. 32% in Q3, driven by AIP platform adoption...",
"detail": "Palantir's commercial segment has posted three consecutive quarters of accelerating growth: 21% → 32% → 54% YoY. This inflection correlates with their AIP (Artificial Intelligence Platform) launch and a shift from government-heavy to commercial-majority revenue...",
"relevance": 0.91, // 0.0-1.0; higher = more actionable for outreach
"relevance_score": 91,
"confidence": "high", // how certain this signal is accurate
"sentiment": "positive",
"signal_category": "growth",
"sales_relevance": "Hypergrowth commercial segment actively expanding vendor relationships for AI infrastructure",
"condition_met": "revenue_growth_accelerating_3q",
"data_source": "quarterly_financials",
"last_refreshed": "2026-03-14T00:00:00Z",
"data_quality_flags": [],
"ticker": "PLTR",
"sector": "Technology",
"country": "US",
"ceo": "Alex Karp",
"size_tier": "mid_cap",
"urgency": "high",
"buyer_persona": ["CTO", "VP Engineering", "Head of Data"],
"signal_strength_label": "strong",
"talk_track": "Palantir's commercial acceleration suggests they're scaling their AI platform rapidly and likely expanding their vendor ecosystem for infrastructure and integrations...",
"recommended_actions": [
"Reference their AIP platform growth in outreach",
"Target commercial team leads who are building integrations",
"Time outreach to align with next earnings in early June"
],
"shelf_life_days": 90,
"earnings_date": "2026-02-18",
"growth_period_end": "2025-12-31",
"is_primary": true,
"expires_at": "2026-06-15T00:00:00Z"
}
}Field Reference
Standard envelope and entity fields are shared across all signals — see Schema and Resolution. The fields below are specific to this signal:
Signal-Specific Fields
The data object contains everything unique to this signal type — the intelligence derived from financial analysis.
| Field | Type | Description |
|---|---|---|
summary | string | One-line headline describing the financial signal (e.g., "Palantir's commercial revenue accelerating — 54% YoY growth in Q4"). Designed to be shown directly to end users. Typically 10–20 words, always includes the company name and the key metric |
detail | string | Multi-sentence analysis explaining the financial trend, what's driving it, and why it matters for sales outreach. Typically 3–5 sentences. Generated by analyzing multiple quarters of financial data against sector benchmarks |
relevance | float (0.0–1.0) | How actionable this signal is for outreach. Higher = stronger commercial signal. Useful for prioritization and filtering |
relevance_score | integer (0–100) | Integer version of relevance for systems that prefer whole numbers. Same meaning as relevance × 100 |
confidence | string | Confidence that the financial condition is real and correctly categorized. high, medium, or low. Based on data completeness and statistical significance. Useful for filtering in production |
sentiment | string | Whether the financial trend is favorable (positive), unfavorable (negative), or informational (neutral) for the company. Useful for segmenting outreach tone |
signal_category | string | Category grouping (e.g., "growth", "distress", "efficiency"). Useful for routing signals to the right sales motion |
sales_relevance | string | Brief phrase describing the outreach angle this signal creates. Useful as a prompt input or display label |
condition_met | string | The specific statistical condition that triggered this signal (e.g., "revenue_growth_accelerating_3q"). Useful for understanding exactly why this signal fired and for building custom filtering logic |
data_source | string | Source of the underlying financial data (e.g., "quarterly_financials", "annual_report"). Useful for data lineage and validation |
last_refreshed | string (datetime) | When the financial data was last updated. Useful for determining freshness |
data_quality_flags | array[string] | Any data quality concerns (e.g., "restated_financials", "estimated_values"). Empty array when data is clean. Useful for filtering out signals built on uncertain data |
ticker | string | Stock ticker symbol. Useful for correlating with market data or financial APIs |
sector | string | Industry sector classification. Useful for peer comparison and sector-based filtering |
country | string | Country of incorporation or primary operations. Useful for territory-based routing |
ceo | string | Current CEO name. Useful for personalized outreach and executive-level targeting |
size_tier | string | Market cap tier: mega_cap, large_cap, mid_cap, small_cap, micro_cap. Useful for segmenting by company size |
urgency | string | How time-sensitive this signal is for outreach: high, medium, low. Based on signal freshness, shelf life, and competitive dynamics |
buyer_persona | array[string] | Recommended personas to target based on this signal (e.g., ["CTO", "VP Engineering"]). Useful for routing to the right sales rep or building persona-based campaigns |
signal_strength_label | string | Human-readable strength: strong, moderate, weak. Combines relevance, confidence, and recency into a single label for display |
talk_track | string | Suggested messaging angle for a sales rep reaching out based on this signal. Written as a starting point for personalized outreach — not a script. Useful for enabling reps who aren't familiar with the account |
recommended_actions | array[string] | Specific next steps a sales rep could take based on this signal. Typically 2-4 items. Useful for driving rep behavior in CRM workflows |
shelf_life_days | integer | How many days this signal remains relevant for outreach from detected_at. After this window, the signal is considered stale. Useful for expiring signals from active queues |
earnings_date | string (date) | Most recent earnings date that informed this signal. Useful for correlating with other earnings-based signals |
growth_period_end | string (date) | End date of the measurement period (e.g., last quarter-end). Useful for understanding which data informed the signal |
is_primary | boolean | Whether this is the strongest/most actionable signal for this company in this batch. When multiple financial signals fire for one company, is_primary: true marks the most important one. Useful for deduplication in high-volume pipelines |
expires_at | string (datetime) | Explicit expiration timestamp for this signal. After this time, the signal should be removed from active workflows. Derived from detected_at + shelf_life_days |
Timing & Delivery
detected_atis when we computed and generated the signal. Usegrowth_period_endandearnings_datefor the underlying data context.- One signal per subtype per company per quarter. Financial conditions are re-evaluated weekly, but a given subtype only fires once per company per fiscal quarter to avoid noise.
- Each delivery arrives in a timestamped folder. Treat all signals in a new folder as recent — no need to diff against prior deliveries.
Coverage
- Refresh: Biweekly
- Coverage: 7,000+ US and international public companies
- Best for: Timing outreach to growth inflections, identifying companies in buying mode, filtering by financial health
Updated 9 days ago